<p>Threshold Effects in the Relationship Between Serum Non-High-Density Lipoprotein Cholesterol and Metabolic Syndrome</p>

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O R I G I N A L R E S E A R C H

Threshold Effects in the Relationship Between Serum

Non-High-Density Lipoprotein Cholesterol and

Metabolic Syndrome

This article was published in the following Dove Press journal:

Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy

Saibin Wang 1

Junwei Tu1 Yibin Pan2

1_{Department of Respiratory Medicine,}

Jinhua Municipal Central Hospital, Jinhua, Zhejiang Province 321000, People’s Republic of China;2_{Department of} Cardiovascular Medicine, Jinhua Municipal Central Hospital, Jinhua, Zhejiang Province 321000, People’s Republic of China

Background:Previous studies have suggested that the non-high-density lipoprotein choles-terol (non-HDL-C) is strongly associated with metabolic syndrome (MetS); however, the explicit relationship between them has not yet been clariﬁed. The aim of this study was to reveal the explicit association between the non-HDL-C with MetS.

Methods:The present study was based on a cross-sectional study, which was carried out in Spain. A total of 60,799 workers were recruited between 2012 and 2016. Anthropometric parameters and blood indices (lipid proﬁle and fasting blood glucose) were collected. Participants were divided into the MetS group or the non-MetS group based on the criteria of the National Cholesterol Education Program Adult Treatment Panel III. The relationship between serum non-HDL-C and the risk of MetS was evaluated using multivariate regression analysis, piece-wise linear regression analysis, smooth curveﬁtting and threshold saturation effect analysis after adjustment of potential confounders.

Results:The risk of developing MetS increased with increasing non-HDL-C level. However, this association was only presented in the range of the non-HDL-C concentrations from 118 mg/ dl to 247 mg/dl after adjusting for potential confounders. When compared to lower non-HDL-C level (<118 mg/dl), higher levels of non-HDL-C (118–247 mg/dl and >247 mg/dl) were related to higher incidence of MetS, with adjusted odds ratio (95% conﬁdence interval) of 3.08 (2.77, 3.42) and 17.18 (14.29, 20.65), respectively (P for trend <0.05).

Conclusion: Higher serum non-HDL-C level was associated with increased MetS inci-dence; however, signiﬁcant threshold saturation effects were observed when the non-HDL-C level <118 mg/dl or >247 mg/dl.

Keywords:non-high-density lipoprotein cholesterol, metabolic syndrome, threshold effect, lipid

Introduction

Metabolic syndrome (MetS) is an array of metabolic abnormalities, mainly char-acterized by central obesity, atherogenic dyslipidemia, hypertension,

hyperglyce-mia, and insulin resistance.1–3 It has been recognized that MetS is related to an

increased risk for several disorders, such as more than twofold increased risk for

cardiovascular disease (CVD) and ﬁvefold increased risk for type 2 diabetes.1

Alarmingly, MetS has come forth with a rapidly increasing incidence worldwide

during the last decades. It was reported that MetS affects more than oneﬁfth of the

population in the United States, a quarter in Europe and 15% in China.3–5

Nevertheless, the pathogenic mechanisms of MetS are still not fully clariﬁed.3,6The mainstay of treatment regimens recommended currently is lifestyle adjustments and

Correspondence: Junwei Tu Department of Respiratory Medicine, Jinhua Municipal Central Hospital, No. 365, East Renmin Road, Jinhua, Zhejiang Province 321000, People’s Republic of China

Tel +86 579 82552278 Fax +86 579 82325006 Email junweiau@126.com

Yibin Pan

Department of Cardiovascular Medicine, Jinhua Municipal Central Hospital, No. 365, East Renmin Road, Jinhua, Zhejiang Province 321000, People’s Republic of China

Tel +86 579 82552926 Fax +86 579 82552927 Email 18851733351@163.com

Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy

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risk factors modiﬁcations.1Atherogenic dyslipidemia is one of the core components of MetS. Conventionally, the role of high-density lipoprotein cholesterol (HDL-C) and triglycer-ides in MetS is often been emphasized, and they have been included in the MetS criteria proposed by multiple authorities (the International Diabetes Federation, the American Heart Association, the National Heart Lung and Blood Institute, the World Heart Federation, the International Atherosclerosis Society, and the International Association for the Study of

Obesity).1Recently, the non-HDL-C level was reported to

have a strong association with MetS.7,8Moreover, the non-HDL-C has showed great value in predicting the risk for

CVD.9–13On the other hand, non-HDL-C has the advantage

of not being inﬂuenced by the prandial situation.14Both good predictive potential and fasting detection make non-HDL-C a promising marker for the risk assessment and prediction of MetS. Therefore, it would be clinically useful to identify the speciﬁc relationship between the non-HDL-C and the risk of MetS.

The hypothesis of the present study was that there are concentration-dependent effect and threshold effect of non-HDL-C levels on the incidence of MetS, and we performed this study based on a previous cross-sectional study of a large sample.

Methods

Study Population and Data Collection

The present study was a secondary analysis of a cross-sectional study conducted by Romero-Saldaña et al in Spain

from 2012 to 2016. In the previously published article,15

Romero-Saldaña et al validated a non-invasive method for the early detection of MetS using the data of 60,799 workers aged 20–70 years from different economic departments, such as public administration and health services. The used data in

this study were obtained from a public database (www.

Datadryad.org) that permits users to freely download raw data.16The data could be used for secondary analysis without infringing on the authors’rights because authors of the origi-nal study have waived all copyright and related ownership of these data on this public database. The following variables were collected in their study: a. anthropometric variables: gender, age, smoking status, systolic blood pressure (SBP), diastolic BP (DBP), waist circumference (WC), body mass index (BMI), waist/height ratio (WHtR), body fat (BF)% (calculated by the formula: BF% = 1.2 × (BMI) + 0.23 × age (years) - 10.8 × gender (male = 1; female = 0) - 5.4), and a body shape index (ABSI) (calculated by the formula: ABSI

= WC/((BMI)2/3(height)1/2)); b. laboratory parameters: total cholesterol (TC), HDL-C, LDL-C, triglycerides, and fasting blood glucose (FBG). In the present study, the non-HDL-C

was calculated as the TC minus HDL-C.7Concentrations of

cholesterol and triglycerides in serum were determined according to the standard procedures of the clinical biochem-ical laboratory using an autoanalyser (SYNCHRON CXH9

PRO, Beckman Coulter, Brea, California, USA).15The ethics

committee of Jinhua Municipal Central Hospital (Jinhua, China) approved this secondary analysis and informed con-sent was waived because all the participant information was anonymous.

In the present study, the diagnosis of MetS was made when participants met three of the followingﬁve criteria proposed by the National Cholesterol Education Program Adult

Treatment Panel III (NCEP-ATP III),17 which were: (1)

Abdominal obesity (WC ≥102 cm in men and≥ 88 cm in

women), (2) Triglycerides≥150 mg/dl, (3) HDL-C < 40 mg/

dl in men and < 50 mg/dl in women, (4) BP≥130/85 mmHg,

and (5) Fasting glucose≥100 mg/dl. Three consecutive BP

measurements were recorded with one interval between two measurements of the subjects in the supine position, and the average value was adopted.15

Statistical Analysis

Descriptive statistics were used to summarize the participant’s anthropometry and laboratory tests. Categorical data were expressed as the number and the percentage, and continuous data were indicated as the mean ± standard deviation. Unpaired

t-test and Pearson chi-squared test were performed between the MetS group and the non-MetS group for comparison. Multiple regression analysis, piece-wise linear regression analysis, smooth curvefitting, and threshold saturation effect analysis were used to evaluate the association between non-HDL-C levels and the risk of MetS, with or without adjustment for potential confounders. In the present study, we performed a multicollinearity test for the selection of adjusted variables. Besides, the following adjustment strategies were adopted: variables producing a change of matched odds ratio (OR) by at least 10% after introducing them into the basic model or removing them from the completed model and variables that judged by clinical significance.18 R software (version 3.5.1) was used for statistical analysis.Pvalue <0.05 was considered statistically significant.

Results

Of the 60,799 participants, 9.0% (5587/60,799) met the diagnostic criterion of MetS according to the NCEP-ATP

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III. Signiﬁcant differences in baseline characteristics and laboratory tests were observed between the MetS group

and the non-Mets group (Table 1). The non-HDL-C

con-centration was signiﬁcantly higher in the MetS group than

in the non-MetS group (Figure 1,P< 0.001).

In the smooth curveﬁtting (Figure 2), a non-linear rela-tionship was observed between the non-HDL-C and the risk

of MetS. Furthermore, signiﬁcant threshold effects were

detected by using a two-piecewise linear regression analysis (Table 2). The risk of developing MetS increased with increasing level of the non-HDL-C above the threshold point I (non-HDL-C concentration = 118 mg/dl) (OR, 1.02; 95% confidence interval [CI], 1.02–1.02; P < 0.01). The threshold point II was detected at the non-HDL-C concentra-tion of 247 mg/dl, above which no statistically significant difference was yielded between increased non-HDL-C level and the risk of MetS (OR, 1.00; 95% CI, 1.00–1.01;P> 0.05). In the unadjusted model, a significant positive correla-tion was observed between the non-HDL-C level and the risk of MetS (P< 0.01). In multicollinearity test, variables

of age, BF, BMI, WHtR, TC and LDL-C wereﬁltered off,

and these variables were not included in the adjustment

analysis except age judged by clinical signiﬁcance.19

Additionally, in order to avoid over-adjustment in the model, variables of SBP, DBP, WC, HDL-C and FBG were also not included in the adjusted model. In adjusted model, after adjusting for gender and age, the strong asso-ciation between them did not change (Table 3,P< 0.01).

Table 1 Baseline Characteristics, Anthropometric Variables and Laboratory Tests of the Study Cohort

Variables The MetS Group (n = 5587)

The Non-MetS group (n = 55,212)

P-value

Gender, n (%) <0.001

Female 1390 (25.3) 24,582 (44.4)

Male 4097 (74.7) 30,730 (55.6)

Age, (year) 47.1 ± 9.3 39.3 ± 10.2 <0.001

Smoking, n (%) <0.001

No 3351 (61.1) 36,271 (65.6)

Yes 2136 (38.9) 19,041 (34.4)

SBP (mmHg) 136.6 ± 16.9 119.2 ± 15.4 <0.001

DBP (mmHg) 83.6 ± 10.9 72.6 ± 10.5 <0.001

BF (%) 34.4 ± 8.0 28.4 ± 7.4 <0.001

ABSI (%) 7.7 ± 0.8 7.3 ± 0.7 <0.001

BMI (kg/m2

) 30.9 ± 5.0 25.6 ± 4.3 <0.001

WC (cm) 97.7 ± 12.9 81.5 ± 10.5 <0.001

WHtR 0.6 ± 0.1 0.5 ± 0.1 <0.001

TC (mg/dL) 222.3 ± 41.3 192.5 ± 36.3 <0.001

HDL-C (mg/dL) 44.1 ± 8.5 53.2 ± 8.2 <0.001

Non-HDL-C (mg/dL) 178.2 ± 43.2 139.3 ± 38.6 <0.001 LDL-C (mg/dL) 133.3 ± 42.9 120.0 ± 36.3 <0.001 Triglycerides (mg/dL) 235.4 ± 139.6 96.8 ± 51.6 <0.001

FBG (mg/dL) 106.6 ± 33.9 86.5 ± 15.7 <0.001

Abbreviations: MetS, metabolic syndrome; SBP, systolic blood pressure; DBP, diastolic blood pressure; BF, body fat; ABSI, a body shape index; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; TC, total choles-terol; HDL-C, high-density lipoprotein cholescholes-terol; LDL-C, low-density lipoprotein cholesterol; FBG, fasting blood glucose.

Figure 1Comparison of serum non-HDL-C concentration between the MetS group and the non-MetS group. The mean non-HDL-C concentration was signiﬁ -cantly higher in the MetS group than that in the non-MetS group. ***P< 0.001.

Abbreviations: HDL-C, high-density lipoprotein cholesterol; MetS, metabolic syndrome.

100 200 300 400 500

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Serum non-HDL-C (mg/dl)

The risk of developing MetS

Figure 2A non-linear association between the risk of MetS and serum non-HDL-C concentration is showed by using a smooth curveﬁtting after adjusting for the potential confounders (gender and age). The threshold saturation effects were observed at 118 mg/dl and 247 mg/dl in a two-piecewise linear regression analysis. Dotted lines represented 95% CI.

Abbreviations:MetS, metabolic syndrome; HDL-C, high-density lipoprotein cho-lesterol; CI, conﬁdence interval.

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Higher levels of non-HDL-C (118–247 mg/dl and >247 mg/

dl) were signiﬁcantly associated with increased risk of

MetS when compared to the lower levels of non-HDL-C (<118 mg/dl) with the OR and 95% CI of 3.08 (2.77, 3.42) and 17.18 (14.29, 20.65), respectively (Pfor trend <0.01).

Discussion

The present study showed that serum non-HDL-C level

was signiﬁcantly associated with the risk of MetS with

threshold effects. Speciﬁcally, it indicated that maintaining serum non-HDL-C concentration at a lower level could help individuals in preventing MetS, and the optimal reference threshold may be at 118 mg/dl.

Nowadays, MetS has emerged as a serious clinical and

public health challenge.2During the past several decades,

the estimated prevalence of MetS in adults varied from one-quarter to one-third based on different age, gender, ethnic backgrounds, socioeconomic status, and the diag-nostic criteria adopted.2Since lifestyle adjustment and risk

modiﬁcation is still the mainstay of treatment for MetS,

early detection and early potential risk factor modiﬁcation become particularly critical.1

Dyslipidemia is an indispensable feature of MetS, and as biomarkers, HDL-C and triglycerides have aroused enough

emphasis on developing MetS.1Recently, the role of

non-HDL-C in developing MetS drew more attention from researchers. It has been reported that the non-HDL-C posi-tively correlated to the prevalence of MetS.7,8 Khan et al found that the non-HDL-C levels were higher in subjects with MetS than those without MetS in a comparative cross-sectional study cohort of 229 participants,7and the authors suggested that, as a predictor for MetS, the non-HDL-C performs better than the LDL-C. In addition, both Al-Hashmi et al9and xiao et al10 in their studies pointed out the non-HDL-C has the potential to be an excellent risk predictor for CVD. Non-HDL-C, as a multi-components lipid parameter, mainly contains LDL-C, very LDL-C, and triglycerides; therefore, it can depict the risk of MetS in a multi-lipids dimensional view in clinical practice. Moreover, Non-HDL-C has the advantage of being detection on non-fasting status, which implies that it would be more convenient than HDL-C, LDL-C, and triglycerides in

application.14 However, the speciﬁc relationship between

non-HDL-C and the developing MetS has not yet been clariﬁed.

In the present study, we conducted a secondary analy-sis of a large cross-sectional study,15 which collected the data from a total of 60,799 workers in Spain. We found

that the serum non-HDL-C level signiﬁcantly associated

with the risk of MetS, either without adjustment or with adjustment for the potential confounders. Not only the non-HDL-C concentration-dependent effect was observed (from 118 mg/dl to 247 mg/dl), but also two threshold effects were identiﬁed, which will help guide lipid control

in preventing MetS. Speciﬁcally, the study showed that

maintaining the non-HDL-C at a lower level is important in decreasing the risk of developing MetS.

Strengths of this study include the inclusion of a large sample analyzed, using multiple statistical meth-ods with variable adjustment for comprehensive analysis of the relationship between the non-HDL-C and the risk

of MetS. However, several limitations to our ﬁndings

are worth noting. First, the participants in this study were recruited from the Caucasian population, as the MetS incidence is associated with the ethnic back-ground; whether the results of our study could apply to individuals of other ethnicities requires further

Table 2 Threshold Effect of Non-HDL-C on the Prevalence of MetS in a Two-Piecewise Linear Regression

Inﬂection Points of Non-HDL -C (mg/dl)

Developing MetSa

OR (95% CI) Pvalue

Inﬂection Point I

<118 1.01 (1.01, 1.02) <0.0001

>118 1.02 (1.02, 1.02) <0.0001

Inﬂection Point II

<247 1.02 (1.02, 1.02) <0.0001

>247 1.00 (1.00, 1.01) 0.0632

Note:a

Adjust for: gender and age.

Abbreviations: MetS, metabolic syndrome; HDL-C, high-density lipoprotein cholesterol.

Table 3Multivariate Regression Analysis of Serum Non-HDL-C with the Risk of Developing MetS

Non-HDL-C (mg/dl)

Developing MetS OR (95% CI)P-value

Non-Adjust Adjust

<118 Ref. Ref.

118–247 5.03 (4.54, 5.57) <0.0001

3.08 (2.77, 3.42) <0.0001

>247 32.15 (26.93, 38.39) <0.0001

17.18 (14.29, 20.65) <0.0001

P for trend <0.0001 <0.0001

Note:Adjust for: gender and age.

Abbreviations: MetS, metabolic syndrome; HDL-C, high-density lipoprotein cholesterol.

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validation. Secondly, we adopted the NCEP-ATP III

deﬁnition of MetS in this study; thus, the results we

observed are only applicable to explain the relationship

between the non-HDL-C and the MetS that deﬁned by

the NCEP-ATP III. Thirdly, this was a secondary analy-sis of a previous study, several potential confounders may need to further adjust, such as lower-lipid therapy, hypotension and hypoglycemic drug use; however, they

were not available in the original data.15

Conclusion

Our ﬁndings revealed that there is a non-linear

associa-tion between the non-HDL-C with the risk of MetS.

Higher non-HDL-C levels were associated with

increased risk of MetS; however, two signiﬁcant

thresh-old saturation effects were observed (at 118 mg/dl and 247 mg/dl). This study suggested that individuals with serum non-HDL-C level between 118 and 247 mg/dl

will signiﬁcantly beneﬁt from non-HDL-C control for

reducing the risk of MetS.

Abbreviations

MetS, metabolic syndrome; SBP, systolic blood pres-sure; DBP, diastolic blood prespres-sure; BF, body fat; ABSI, a body shape index; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; FBG, fasting blood glucose; OR, odds ratio; CI,

con-ﬁdence interval.

Ethics Approval and Consent to

Participate

This study was approved by the ethics committee of Jinhua Municipal Central Hospital (Jinhua, China). Informed consent was waived because all the participant information was anonymous.

Availability of Data and Materials

The data used in this study can be downloaded from the

“DATADRYAD”database (www.Datadryad.org).16

Acknowledgements

We appreciate all the participants involved in the study and the data providers of the study.15

Author Contributions

All authors contributed to data analysis, drafting or revising the article, gaveﬁnal approval of the version to be published, and agreed to be accountable for all aspects of the work.

Funding

This study was supported by the Science and Technology Key Project of Jinhua City (No. 20163011 to S. W.), the Science and Technology Project of Zhejiang Province (No. 2017C37147 to S. W.), the Medical and Health Science and Technology Plan Project of Zhejiang Province (No. 2018KY861 to J. T.), and the Science and Technology Project of Jinhua City (No. 20164024 to Y. P.).

Disclosure

The authors declare no conﬂicts of interest in this work.

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